import torch
import torch.nn as nn
import torch.optim as optim
import torch.autograd as autograd 
import random
import math
class CNN(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(CNN, self).__init__()
        
        self.input_dim = input_dim
        self.output_dim = output_dim
        
        self.features = nn.Sequential(
            nn.Conv2d(input_dim[0], 32, kernel_size=8, stride=4),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=4, stride=2),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, stride=1),
            nn.ReLU()
        )
        
        self.fc = nn.Sequential(
            nn.Linear(self.feature_size(), 512),
            nn.ReLU(),
            nn.Linear(512, self.output_dim)
        )
        
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x
    
    def feature_size(self):
        return self.features(autograd.Variable(torch.zeros(1, *self.input_dim))).view(1, -1).size(1)


    def act(self, state, epsilon):
        if random.random() > epsilon:
            state   = Variable(torch.FloatTensor(np.float32(state)).unsqueeze(0), volatile=True)
            q_value = self.forward(state)
            action  = q_value.max(1)[1].data[0]
        else:
            action = random.randrange(env.action_space.n)
        return action

class ReplayBuffer:
    def __init__(self, capacity):
        self.capacity = capacity # 经验回放的容量
        self.buffer = [] # 缓冲区
        self.position = 0 
    
    def push(self, state, action, reward, next_state, done):
        ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition)
        '''
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[self.position] = (state, action, reward, next_state, done)
        self.position = (self.position + 1) % self.capacity 
    
    def sample(self, batch_size):
        batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移
        state, action, reward, next_state, done =  zip(*batch) # 解压成状态，动作等
        return state, action, reward, next_state, done
    
    def __len__(self):
        ''' 返回当前存储的量
        '''
        return len(self.buffer)

class DQN:
    def __init__(self, n_states, n_actions, cfg):

        self.n_actions = n_actions  # 总的动作个数
        self.device = cfg.device  # 设备，cpu或gpu等
        self.gamma = cfg.gamma  # 奖励的折扣因子
        # e-greedy策略相关参数
        self.frame_idx = 0  # 用于epsilon的衰减计数
        self.epsilon = lambda frame_idx: cfg.epsilon_end + \
            (cfg.epsilon_start - cfg.epsilon_end) * \
            math.exp(-1. * frame_idx / cfg.epsilon_decay)
        self.batch_size = cfg.batch_size
        self.policy_net = CNN(n_states, n_actions).to(self.device)
        self.target_net = CNN(n_states, n_actions).to(self.device)
        for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net
            target_param.data.copy_(param.data)
        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器
        self.memory = ReplayBuffer(cfg.memory_capacity) # 经验回放

    def choose_action(self, state):
        ''' 选择动作
        '''
        self.frame_idx += 1
        if random.random() > self.epsilon(self.frame_idx):
            with torch.no_grad():
                print(type(state))
                state = torch.tensor([state], device=self.device, dtype=torch.float32)
                q_values = self.policy_net(state)
                action = q_values.max(1)[1].item() # 选择Q值最大的动作
        else:
            action = random.randrange(self.n_actions)
        return action
    def update(self):
        if len(self.memory) < self.batch_size: # 当memory中不满足一个批量时，不更新策略
            return
        # 从经验回放中(replay memory)中随机采样一个批量的转移(transition)
        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
            self.batch_size)
        # 转为张量
        state_batch = torch.tensor(state_batch, device=self.device, dtype=torch.float)
        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1)  
        reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float)  
        next_state_batch = torch.tensor(next_state_batch, device=self.device, dtype=torch.float)
        done_batch = torch.tensor(np.float32(done_batch), device=self.device)
        q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 计算当前状态(s_t,a)对应的Q(s_t, a)
        next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 计算下一时刻的状态(s_t_,a)对应的Q值
        # 计算期望的Q值，对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
        expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch)
        loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))  # 计算均方根损失
        # 优化更新模型
        self.optimizer.zero_grad()  
        loss.backward()
        for param in self.policy_net.parameters():  # clip防止梯度爆炸
            param.grad.data.clamp_(-1, 1)
        self.optimizer.step() 

    def save(self, path):
        torch.save(self.target_net.state_dict(), path+'dqn_checkpoint.pth')

    def load(self, path):
        self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))
        for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
            param.data.copy_(target_param.data)